Springer Protocols: Abstract: Building a Meta-Predictor for MHC Class II-Binding Peptides

Building a Meta-Predictor for MHC Class II-Binding Peptides

By: Lei Huang³, Oleksiy Karpenko³, Naveen Murugan³, Yang Dai³

Abstract

Full Text

Download PDF (301K)

Prediction of class II major histocompatibility complex (MHC)–peptide binding is a challenging task due to variable length of binding peptides. Different computational methods have been developed; however, each has its own strength and weakness. In order to provide reliable prediction, it is important to design a system that enables the integration of outcomes from various predictors. In this chapter, the procedure of building such a meta-predictor based on Naïve Bayesian approach is introduced. The system is designed in such a way that results obtained from any number of individual predictors can be easily incorporated. This meta-predictor is expected to give users more confidence in the prediction.

Affiliation(s): (3) Bioengineering Bioinformatics, University of Illinois at Chicago, Chicago, 851, South Morgan Street, 60607, IL, USA

Book Title: Immunoinformatics: Predicting Immunogenicity In Silico

Series: Methods in Molecular Biology | Volume: 409 | Pub. Date: Jun-21-2007 | Page Range: 355-364 | DOI: 10.1007/978-1-60327-118-9_26

Subject: Immunology

Key Words: MHC class II binding - epitope prediction - meta-predictor - Naïve Bayesian classifier

References

Download References

1.	Flower, D. R. (2004) Vaccines in silico – the growth and power of immunoinformatics. The Biochemist 26, 17–20.

2.	De Groot, A. S. and Berzofsky, J. A. (2004) From genome to vaccine – new immunoinformatics tools for vaccine design. Methods 34, 425–428.

3.	Parham, P. (2005) The Immune System. Garland Science, New York, NY.

4.	Castellino, F., Zhong, G., and Germain, R. N. (1997) Antigen presentation by MHC class II molecules: invariant chain function, protein trafficking, and the molecular basis of diverse determinant capture. Hum. Immunol. 54, 159–169.

5.	Sette, A., Buus, S., Appella, E., Smith, J. A., Chesnut, R., Miles, C., Colon, S. M., and Grey, H. M. (1989) Prediction of major histocompatibility complex binding regions of protein antigens by sequence pattern analysis. Proc. Natl. Acad. Sci. U.S.A. 86, 3296–3300.

6.	Max, H., Halder, T., Kropshofer, H., Kalbus, M., Muller, C. A., and Kalbacher, H. (1993) Characterization of peptides bound to extracellular and intracellular HLA-DR1 molecules. Hum. Immunol. 38, 193–200.

7.	Rammensee, H., Bachmann, J., Emmerich, N. P., Bachor, O. A., and Stevanovic, S. (1999) SYFPEITHI: database for MHC ligands and peptide motifs. Immunogenetics 50, 213–219.

8.	Borras-Cuesta, F., Golvano, J., Garcia-Granero, M., Sarobe, P., Riezu-Boj, J., Huarte, E., and Lasarte, J. (2000) Specific and general HLA-DR binding motifs: comparison of algorithms. Hum. Immunol. 61, 266–278.

9.	Sturniolo, T., Bono, E., Ding, J., Raddrizzani, L., Tuereci, O., Sahin, U., Braxenthaler, M., Gallazzi, F., Protti, M. P., Sinigaglia, F., and Hammer, J. (1999) Generation of tissue-specific and promiscuous HLA ligand databases using DNA microarrays and virtual HLA class II matrices. Nat. Biotechnol. 17, 555–561.

10.	Kato, R., Noguchi, H., Honda, H., and Kobayashi, T. (2003) Hidden Markov model-based approach as the first screening of binding peptides that interact with MHC class II molecules. Enzyme Microb. Technol. 33, 472–481.

11.	Brusic, V., Rudy, G., Honeyman, G., Hammer, J., and Harrison, L. (1998) Prediction of MHC class II-binding peptides using an evolutionary algorithm and artificial neural network. Bioinformatics 14, 121–130.

12.	Nielsen, M., Lundegaard, C., Worning, P., Lauemoller, S. L., Lamberth, K., Buus, S., Brunak, S., and Lund, O. (2003) Reliable prediction of T-cell epitopes using neural networks with novel sequence representations. Protein Sci. 12, 1007–1017.

13.	Bhasin, M. and Raghava, G. P. (2004) SVM based method for predicting HLA-DRB1^0401 binding peptides in an antigen sequence. Bioinformatics* 20, 421–423.

14.	Theodoridis, S. and Koutroumbas, K. (1999) Pattern Recognition. Academic Press, San Diego, CA.

15.	Singh, H. and Raghava, G. P. (2001) ProPred: prediction of HLA-DR binding sites. Bioinformatics 17, 1236–1237.

16.	Nielsen, M., Lundegaard, C., Worning, P., Hvid, C. S., Lamberth, K., Buus, S., Brunak, S., and Lund, O. (2004) Improved prediction of MHC class I and class II epitopes using a novel Gibbs sampling approach. Bioinformatics 20, 1388–1397.

17.	Murugan, N. and Dai, Y. (2005) Prediction of MHC class II binding peptides based on an iterative learning model. Immunome Res. 1, 6.

18.	Toseland, C. P., Clayton, D. J., McSparron, H., Hemsley, S. L., Blythe, M. J., Paine, K., Doytchinova, I. A., Guan, P., Hattotuwagama, C. K., and Flower, D. R. (2005) AntiJen: a quantitative immunology database integrating functional, thermodynamic, kinetic, biophysical, and cellular data. Immunome Res. 1, 4.

19.	Bhasin, M., Singh, H., and Raghava, G. P. (2003) MHCBN: a comprehensive database of MHC binding and non-binding peptides. Bioinformatics 19, 665–666.

20.	Swets, J. A. (1988) Measuring the accuracy of diagnostic systems. Science 240, 1285–1293.

Comments (Loading...)

Post comment/View all comments